Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations1296675
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory123.7 MiB
Average record size in memory100.0 B

Variable types

Numeric14
Categorical2

Alerts

lat is highly overall correlated with merch_latHigh correlation
long is highly overall correlated with merch_long and 1 other fieldsHigh correlation
merch_lat is highly overall correlated with latHigh correlation
merch_long is highly overall correlated with long and 1 other fieldsHigh correlation
trans_date_trans_time is highly overall correlated with unix_timeHigh correlation
unix_time is highly overall correlated with trans_date_trans_timeHigh correlation
zip is highly overall correlated with long and 1 other fieldsHigh correlation
is_fraud is highly imbalanced (94.9%)Imbalance
amt is highly skewed (γ1 = 42.27787379)Skewed
trans_date_trans_time is uniformly distributedUniform
trans_num is uniformly distributedUniform
trans_num has unique valuesUnique
category has 94014 (7.3%) zerosZeros

Reproduction

Analysis started2024-09-15 11:15:33.219358
Analysis finished2024-09-15 11:16:59.923092
Duration1 minute and 26.7 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

trans_date_trans_time
Real number (ℝ)

HIGH CORRELATION  UNIFORM 

Distinct1274791
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean637735.05
Minimum0
Maximum1274790
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T16:47:00.009302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile64078.7
Q1319473.5
median638093
Q3955484.5
95-th percentile1211123.3
Maximum1274790
Range1274790
Interquartile range (IQR)636011

Descriptive statistics

Standard deviation367716.4
Coefficient of variation (CV)0.57659745
Kurtosis-1.1981127
Mean637735.05
Median Absolute Deviation (MAD)318013
Skewness-0.0010780392
Sum8.2693509 × 1011
Variance1.3521535 × 1011
MonotonicityNot monotonic
2024-09-15T16:47:00.162161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219657 4
 
< 0.1%
1218320 4
 
< 0.1%
1223720 4
 
< 0.1%
743811 3
 
< 0.1%
774485 3
 
< 0.1%
812260 3
 
< 0.1%
899009 3
 
< 0.1%
1254673 3
 
< 0.1%
567996 3
 
< 0.1%
416086 3
 
< 0.1%
Other values (1274781) 1296642
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
1274790 1
< 0.1%
1274789 1
< 0.1%
1274788 1
< 0.1%
1274787 1
< 0.1%
1274786 1
< 0.1%
1274785 1
< 0.1%
1274784 1
< 0.1%
1274783 1
< 0.1%
1274782 1
< 0.1%
1274781 1
< 0.1%

cc_num
Real number (ℝ)

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1719204 × 1017
Minimum6.0416207 × 1010
Maximum4.9923464 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T16:47:00.629625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6.0416207 × 1010
5-th percentile6.3048488 × 1011
Q11.8004295 × 1014
median3.5214173 × 1015
Q34.6422555 × 1015
95-th percentile4.497914 × 1018
Maximum4.9923464 × 1018
Range4.9923463 × 1018
Interquartile range (IQR)4.4622125 × 1015

Descriptive statistics

Standard deviation1.3088064 × 1018
Coefficient of variation (CV)3.1371798
Kurtosis6.1799499
Mean4.1719204 × 1017
Median Absolute Deviation (MAD)3.0764709 × 1015
Skewness2.851879
Sum-6.7255419 × 1018
Variance1.7129743 × 1036
MonotonicityNot monotonic
2024-09-15T16:47:00.771785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.713652351 × 10113123
 
0.2%
4.512828415 × 10183123
 
0.2%
3.672269902 × 10133119
 
0.2%
2.131124026 × 10143117
 
0.2%
3.54510934 × 10153113
 
0.2%
6.534628261 × 10153112
 
0.2%
6.011367958 × 10153110
 
0.2%
2.720433096 × 10153107
 
0.2%
6.011438889 × 10153106
 
0.2%
6.011109737 × 10153101
 
0.2%
Other values (973) 1265544
97.6%
ValueCountFrequency (%)
6.041620718 × 10101518
0.1%
6.042292873 × 10101531
0.1%
6.042309813 × 1010510
 
< 0.1%
6.042785159 × 1010528
 
< 0.1%
6.048700208 × 1010496
 
< 0.1%
6.04905963 × 10101010
0.1%
6.049559311 × 1010518
 
< 0.1%
5.018029536 × 10111559
0.1%
5.018181333 × 10118
 
< 0.1%
5.018282048 × 1011515
 
< 0.1%
ValueCountFrequency (%)
4.992346398 × 10182059
0.2%
4.989847571 × 10181007
 
0.1%
4.980323468 × 1018532
 
< 0.1%
4.973530368 × 10181040
0.1%
4.958589672 × 10181476
0.1%
4.95682899 × 10182566
0.2%
4.911818931 × 10189
 
< 0.1%
4.906628656 × 10182584
0.2%
4.897067971 × 10181038
0.1%
4.890424427 × 10181496
0.1%

merchant
Real number (ℝ)

Distinct693
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342.85849
Minimum0
Maximum692
Zeros1844
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T16:47:00.911591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q1165
median346
Q3514
95-th percentile659
Maximum692
Range692
Interquartile range (IQR)349

Descriptive statistics

Standard deviation200.9519
Coefficient of variation (CV)0.58610739
Kurtosis-1.215053
Mean342.85849
Median Absolute Deviation (MAD)175
Skewness0.0086598641
Sum4.4457604 × 108
Variance40381.666
MonotonicityNot monotonic
2024-09-15T16:47:01.054973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316 4403
 
0.3%
105 3649
 
0.3%
571 3634
 
0.3%
349 3510
 
0.3%
70 3493
 
0.3%
136 3434
 
0.3%
117 2736
 
0.2%
358 2734
 
0.2%
463 2723
 
0.2%
607 2721
 
0.2%
Other values (683) 1263638
97.5%
ValueCountFrequency (%)
0 1844
0.1%
1 1763
0.1%
2 1751
0.1%
3 1895
0.1%
4 940
 
0.1%
5 1746
0.1%
6 1904
0.1%
7 2503
0.2%
8 1923
0.1%
9 821
 
0.1%
ValueCountFrequency (%)
692 1783
0.1%
691 2560
0.2%
690 1695
0.1%
689 1804
0.1%
688 1297
0.1%
687 2017
0.2%
686 1870
0.1%
685 1766
0.1%
684 1872
0.1%
683 2358
0.2%

category
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2277872
Minimum0
Maximum13
Zeros94014
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T16:47:01.179295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q310
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.9134428
Coefficient of variation (CV)0.62838416
Kurtosis-1.1947613
Mean6.2277872
Median Absolute Deviation (MAD)4
Skewness0.04905729
Sum8075416
Variance15.315035
MonotonicityNot monotonic
2024-09-15T16:47:01.293371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 131659
10.2%
4 123638
9.5%
6 123115
9.5%
12 116672
9.0%
7 113035
8.7%
11 97543
7.5%
0 94014
7.3%
1 91461
 
7.1%
10 90758
 
7.0%
5 85879
 
6.6%
Other values (4) 228901
17.7%
ValueCountFrequency (%)
0 94014
7.3%
1 91461
7.1%
2 131659
10.2%
3 45452
 
3.5%
4 123638
9.5%
5 85879
6.6%
6 123115
9.5%
7 113035
8.7%
8 63287
4.9%
9 79655
6.1%
ValueCountFrequency (%)
13 40507
 
3.1%
12 116672
9.0%
11 97543
7.5%
10 90758
7.0%
9 79655
6.1%
8 63287
4.9%
7 113035
8.7%
6 123115
9.5%
5 85879
6.6%
4 123638
9.5%

amt
Real number (ℝ)

SKEWED 

Distinct52928
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.351035
Minimum1
Maximum28948.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T16:47:01.420083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.44
Q19.65
median47.52
Q383.14
95-th percentile196.31
Maximum28948.9
Range28947.9
Interquartile range (IQR)73.49

Descriptive statistics

Standard deviation160.31604
Coefficient of variation (CV)2.2788014
Kurtosis4545.645
Mean70.351035
Median Absolute Deviation (MAD)37.5
Skewness42.277874
Sum91222429
Variance25701.232
MonotonicityNot monotonic
2024-09-15T16:47:01.559498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.14 542
 
< 0.1%
1.04 538
 
< 0.1%
1.25 535
 
< 0.1%
1.02 533
 
< 0.1%
1.01 523
 
< 0.1%
1.05 519
 
< 0.1%
1.2 516
 
< 0.1%
1.23 515
 
< 0.1%
1.08 512
 
< 0.1%
1.11 509
 
< 0.1%
Other values (52918) 1291433
99.6%
ValueCountFrequency (%)
1 222
< 0.1%
1.01 523
< 0.1%
1.02 533
< 0.1%
1.03 499
< 0.1%
1.04 538
< 0.1%
1.05 519
< 0.1%
1.06 471
< 0.1%
1.07 498
< 0.1%
1.08 512
< 0.1%
1.09 496
< 0.1%
ValueCountFrequency (%)
28948.9 1
< 0.1%
27390.12 1
< 0.1%
27119.77 1
< 0.1%
26544.12 1
< 0.1%
25086.94 1
< 0.1%
17897.24 1
< 0.1%
15305.95 1
< 0.1%
15047.03 1
< 0.1%
15034.18 1
< 0.1%
14849.74 1
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
709863 
1
586812 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 709863
54.7%
1 586812
45.3%

Length

2024-09-15T16:47:01.690151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T16:47:01.784286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 709863
54.7%
1 586812
45.3%

Most occurring characters

ValueCountFrequency (%)
0 709863
54.7%
1 586812
45.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 709863
54.7%
1 586812
45.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1296675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 709863
54.7%
1 586812
45.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 709863
54.7%
1 586812
45.3%

street
Real number (ℝ)

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean488.03441
Minimum0
Maximum982
Zeros2602
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T16:47:01.900442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48
Q1252
median485
Q3720
95-th percentile929
Maximum982
Range982
Interquartile range (IQR)468

Descriptive statistics

Standard deviation280.06084
Coefficient of variation (CV)0.57385469
Kurtosis-1.1723021
Mean488.03441
Median Absolute Deviation (MAD)235
Skewness0.0082662276
Sum6.3282202 × 108
Variance78434.073
MonotonicityNot monotonic
2024-09-15T16:47:02.045047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 3123
 
0.2%
859 3123
 
0.2%
814 3119
 
0.2%
472 3117
 
0.2%
805 3113
 
0.2%
303 3112
 
0.2%
160 3110
 
0.2%
848 3107
 
0.2%
417 3106
 
0.2%
601 3101
 
0.2%
Other values (973) 1265544
97.6%
ValueCountFrequency (%)
0 2602
0.2%
1 1523
0.1%
2 972
 
0.1%
3 11
 
< 0.1%
4 1019
 
0.1%
5 1542
0.1%
6 3123
0.2%
7 524
 
< 0.1%
8 2032
0.2%
9 2036
0.2%
ValueCountFrequency (%)
982 1035
0.1%
981 1022
0.1%
980 521
 
< 0.1%
979 504
 
< 0.1%
978 8
 
< 0.1%
977 2553
0.2%
976 1518
0.1%
975 2021
0.2%
974 998
 
0.1%
973 523
 
< 0.1%

city
Real number (ℝ)

Distinct894
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean445.26328
Minimum0
Maximum893
Zeros532
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T16:47:02.184430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46
Q1224
median439
Q3677
95-th percentile843
Maximum893
Range893
Interquartile range (IQR)453

Descriptive statistics

Standard deviation258.60012
Coefficient of variation (CV)0.58078024
Kurtosis-1.2142164
Mean445.26328
Median Absolute Deviation (MAD)225
Skewness0.0094241413
Sum5.7736177 × 108
Variance66874.021
MonotonicityNot monotonic
2024-09-15T16:47:02.323194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 5617
 
0.4%
725 5130
 
0.4%
821 5105
 
0.4%
644 5075
 
0.4%
501 5060
 
0.4%
796 4634
 
0.4%
171 4613
 
0.4%
154 4604
 
0.4%
837 4599
 
0.4%
359 4168
 
0.3%
Other values (884) 1248070
96.3%
ValueCountFrequency (%)
0 532
 
< 0.1%
1 2097
0.2%
2 516
 
< 0.1%
3 2035
0.2%
4 511
 
< 0.1%
5 1056
0.1%
6 1025
0.1%
7 1019
0.1%
8 1034
0.1%
9 1049
0.1%
ValueCountFrequency (%)
893 1537
0.1%
892 1557
0.1%
891 525
 
< 0.1%
890 2109
0.2%
889 514
 
< 0.1%
888 511
 
< 0.1%
887 537
 
< 0.1%
886 1055
0.1%
885 1040
0.1%
884 11
 
< 0.1%

zip
Real number (ℝ)

HIGH CORRELATION 

Distinct970
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48800.671
Minimum1257
Maximum99783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T16:47:02.465712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1257
5-th percentile7208
Q126237
median48174
Q372042
95-th percentile94569
Maximum99783
Range98526
Interquartile range (IQR)45805

Descriptive statistics

Standard deviation26893.222
Coefficient of variation (CV)0.55108305
Kurtosis-1.0964493
Mean48800.671
Median Absolute Deviation (MAD)23068
Skewness0.079680758
Sum6.327861 × 1010
Variance7.2324542 × 108
MonotonicityNot monotonic
2024-09-15T16:47:02.605998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73754 3646
 
0.3%
34112 3613
 
0.3%
48088 3597
 
0.3%
82514 3527
 
0.3%
49628 3123
 
0.2%
15484 3123
 
0.2%
85173 3119
 
0.2%
29819 3117
 
0.2%
38761 3113
 
0.2%
5461 3112
 
0.2%
Other values (960) 1263585
97.4%
ValueCountFrequency (%)
1257 2023
0.2%
1330 1031
 
0.1%
1535 515
 
< 0.1%
1545 1024
 
0.1%
1612 519
 
< 0.1%
1843 2597
0.2%
1844 2058
0.2%
2180 519
 
< 0.1%
2630 2090
0.2%
2908 550
 
< 0.1%
ValueCountFrequency (%)
99783 1568
0.1%
99747 12
 
< 0.1%
99746 540
 
< 0.1%
99323 2572
0.2%
99160 3030
0.2%
99116 15
 
< 0.1%
99113 1047
 
0.1%
99033 2458
0.2%
98836 524
 
< 0.1%
98665 500
 
< 0.1%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct968
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537622
Minimum20.0271
Maximum66.6933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T16:47:02.740744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile29.8826
Q134.6205
median39.3543
Q341.9404
95-th percentile45.8433
Maximum66.6933
Range46.6662
Interquartile range (IQR)7.3199

Descriptive statistics

Standard deviation5.0758084
Coefficient of variation (CV)0.13171047
Kurtosis0.81296795
Mean38.537622
Median Absolute Deviation (MAD)3.3597
Skewness-0.18602768
Sum49970771
Variance25.763831
MonotonicityNot monotonic
2024-09-15T16:47:02.879102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.385 3646
 
0.3%
26.1184 3613
 
0.3%
42.5164 3597
 
0.3%
43.0048 3527
 
0.3%
39.8936 3123
 
0.2%
44.5995 3123
 
0.2%
33.2887 3119
 
0.2%
34.0326 3117
 
0.2%
33.4783 3113
 
0.2%
44.3346 3112
 
0.2%
Other values (958) 1263585
97.4%
ValueCountFrequency (%)
20.0271 1527
0.1%
20.0827 1032
 
0.1%
24.6557 2584
0.2%
26.1184 3613
0.3%
26.3304 542
 
< 0.1%
26.3771 518
 
< 0.1%
26.4215 3038
0.2%
26.4722 2524
0.2%
26.529 1549
0.1%
26.6939 1027
 
0.1%
ValueCountFrequency (%)
66.6933 12
 
< 0.1%
65.6899 540
 
< 0.1%
64.7556 1568
0.1%
48.8878 3030
0.2%
48.8856 2066
0.2%
48.8328 1533
0.1%
48.6669 1047
 
0.1%
48.6031 2973
0.2%
48.4786 2038
0.2%
48.34 3088
0.2%

long
Real number (ℝ)

HIGH CORRELATION 

Distinct969
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226335
Minimum-165.6723
Maximum-67.9503
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2024-09-15T16:47:03.014857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-119.0825
Q1-96.798
median-87.4769
Q3-80.158
95-th percentile-73.5112
Maximum-67.9503
Range97.722
Interquartile range (IQR)16.64

Descriptive statistics

Standard deviation13.759077
Coefficient of variation (CV)-0.15249513
Kurtosis1.8558923
Mean-90.226335
Median Absolute Deviation (MAD)8.1527
Skewness-1.1501077
Sum-1.1699423 × 108
Variance189.3122
MonotonicityNot monotonic
2024-09-15T16:47:03.150189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-98.0727 3646
 
0.3%
-81.7361 3613
 
0.3%
-82.9832 3597
 
0.3%
-108.8964 3527
 
0.3%
-79.7856 3123
 
0.2%
-86.2141 3123
 
0.2%
-111.0985 3119
 
0.2%
-82.2027 3117
 
0.2%
-90.5142 3113
 
0.2%
-73.098 3112
 
0.2%
Other values (959) 1263585
97.4%
ValueCountFrequency (%)
-165.6723 1568
0.1%
-156.292 540
 
< 0.1%
-155.488 1032
0.1%
-155.3697 1527
0.1%
-153.994 12
 
< 0.1%
-124.4409 1043
0.1%
-124.2174 1547
0.1%
-124.1587 1031
0.1%
-124.1437 1526
0.1%
-123.9743 2036
0.2%
ValueCountFrequency (%)
-67.9503 2080
0.2%
-68.5565 1014
 
0.1%
-69.2675 519
 
< 0.1%
-69.4828 2050
0.2%
-69.9576 537
 
< 0.1%
-69.9656 3107
0.2%
-70.1031 9
 
< 0.1%
-70.239 1036
 
0.1%
-70.3001 2090
0.2%
-70.3457 1527
0.1%

trans_num
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1296675
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean648337
Minimum0
Maximum1296674
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T16:47:03.324389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile64833.7
Q1324168.5
median648337
Q3972505.5
95-th percentile1231840.3
Maximum1296674
Range1296674
Interquartile range (IQR)648337

Descriptive statistics

Standard deviation374317.97
Coefficient of variation (CV)0.57735094
Kurtosis-1.2
Mean648337
Median Absolute Deviation (MAD)324169
Skewness-7.6039483 × 10-19
Sum8.4068238 × 1011
Variance1.4011395 × 1011
MonotonicityNot monotonic
2024-09-15T16:47:03.537167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56438 1
 
< 0.1%
1014647 1
 
< 0.1%
134491 1
 
< 0.1%
351341 1
 
< 0.1%
479139 1
 
< 0.1%
395277 1
 
< 0.1%
426124 1
 
< 0.1%
224596 1
 
< 0.1%
565142 1
 
< 0.1%
1009881 1
 
< 0.1%
Other values (1296665) 1296665
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
1296674 1
< 0.1%
1296673 1
< 0.1%
1296672 1
< 0.1%
1296671 1
< 0.1%
1296670 1
< 0.1%
1296669 1
< 0.1%
1296668 1
< 0.1%
1296667 1
< 0.1%
1296666 1
< 0.1%
1296665 1
< 0.1%

unix_time
Real number (ℝ)

HIGH CORRELATION 

Distinct1274823
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3492436 × 109
Minimum1.325376 × 109
Maximum1.3718168 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T16:47:03.682531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.325376 × 109
5-th percentile1.328672 × 109
Q11.3387507 × 109
median1.3492497 × 109
Q31.3593854 × 109
95-th percentile1.3698306 × 109
Maximum1.3718168 × 109
Range46440799
Interquartile range (IQR)20634633

Descriptive statistics

Standard deviation12841278
Coefficient of variation (CV)0.0095173904
Kurtosis-1.0875405
Mean1.3492436 × 109
Median Absolute Deviation (MAD)10358807
Skewness0.0033779498
Sum1.7495305 × 1015
Variance1.6489843 × 1014
MonotonicityIncreasing
2024-09-15T16:47:03.822784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1370177227 4
 
< 0.1%
1335110521 4
 
< 0.1%
1370050667 4
 
< 0.1%
1367602155 3
 
< 0.1%
1364686521 3
 
< 0.1%
1369587838 3
 
< 0.1%
1337306743 3
 
< 0.1%
1343668520 3
 
< 0.1%
1341944714 3
 
< 0.1%
1340650327 3
 
< 0.1%
Other values (1274813) 1296642
> 99.9%
ValueCountFrequency (%)
1325376018 1
< 0.1%
1325376044 1
< 0.1%
1325376051 1
< 0.1%
1325376076 1
< 0.1%
1325376186 1
< 0.1%
1325376248 1
< 0.1%
1325376282 1
< 0.1%
1325376308 1
< 0.1%
1325376318 1
< 0.1%
1325376361 1
< 0.1%
ValueCountFrequency (%)
1371816817 1
< 0.1%
1371816816 1
< 0.1%
1371816752 1
< 0.1%
1371816739 1
< 0.1%
1371816728 1
< 0.1%
1371816696 1
< 0.1%
1371816683 1
< 0.1%
1371816656 1
< 0.1%
1371816562 1
< 0.1%
1371816522 1
< 0.1%

merch_lat
Real number (ℝ)

HIGH CORRELATION 

Distinct1247805
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537338
Minimum19.027785
Maximum67.510267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T16:47:03.973718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum19.027785
5-th percentile29.751653
Q134.733572
median39.36568
Q341.957164
95-th percentile46.00353
Maximum67.510267
Range48.482482
Interquartile range (IQR)7.223592

Descriptive statistics

Standard deviation5.1097884
Coefficient of variation (CV)0.13259318
Kurtosis0.79599391
Mean38.537338
Median Absolute Deviation (MAD)3.397536
Skewness-0.18191543
Sum49970403
Variance26.109937
MonotonicityNot monotonic
2024-09-15T16:47:04.114025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.305966 4
 
< 0.1%
41.937796 4
 
< 0.1%
42.265012 4
 
< 0.1%
41.301611 4
 
< 0.1%
34.134994 4
 
< 0.1%
37.669788 4
 
< 0.1%
39.348185 4
 
< 0.1%
32.64469 4
 
< 0.1%
42.749184 4
 
< 0.1%
38.050673 4
 
< 0.1%
Other values (1247795) 1296635
> 99.9%
ValueCountFrequency (%)
19.027785 1
< 0.1%
19.027804 1
< 0.1%
19.029798 1
< 0.1%
19.031242 1
< 0.1%
19.032277 1
< 0.1%
19.033288 1
< 0.1%
19.034282 1
< 0.1%
19.034687 1
< 0.1%
19.035472 1
< 0.1%
19.036312 1
< 0.1%
ValueCountFrequency (%)
67.510267 1
< 0.1%
67.441518 1
< 0.1%
67.397018 1
< 0.1%
67.188111 1
< 0.1%
67.064277 1
< 0.1%
66.835174 1
< 0.1%
66.682905 1
< 0.1%
66.67355 1
< 0.1%
66.664673 1
< 0.1%
66.659242 1
< 0.1%

merch_long
Real number (ℝ)

HIGH CORRELATION 

Distinct1275745
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226465
Minimum-166.67124
Maximum-66.950902
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2024-09-15T16:47:04.251881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-166.67124
5-th percentile-119.33009
Q1-96.897276
median-87.438392
Q3-80.236796
95-th percentile-73.354218
Maximum-66.950902
Range99.72034
Interquartile range (IQR)16.660479

Descriptive statistics

Standard deviation13.771091
Coefficient of variation (CV)-0.15262806
Kurtosis1.8484792
Mean-90.226465
Median Absolute Deviation (MAD)8.227889
Skewness-1.1469599
Sum-1.169944 × 108
Variance189.64294
MonotonicityNot monotonic
2024-09-15T16:47:04.388376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.116414 4
 
< 0.1%
-81.219189 4
 
< 0.1%
-74.618269 4
 
< 0.1%
-85.326323 3
 
< 0.1%
-84.890305 3
 
< 0.1%
-88.49309 3
 
< 0.1%
-84.100102 3
 
< 0.1%
-97.527227 3
 
< 0.1%
-85.3444 3
 
< 0.1%
-86.037494 3
 
< 0.1%
Other values (1275735) 1296642
> 99.9%
ValueCountFrequency (%)
-166.671242 1
< 0.1%
-166.670132 1
< 0.1%
-166.669638 1
< 0.1%
-166.666179 1
< 0.1%
-166.664828 1
< 0.1%
-166.662888 1
< 0.1%
-166.661968 1
< 0.1%
-166.659277 1
< 0.1%
-166.657834 1
< 0.1%
-166.657174 1
< 0.1%
ValueCountFrequency (%)
-66.950902 1
< 0.1%
-66.955996 1
< 0.1%
-66.95654 1
< 0.1%
-66.958659 1
< 0.1%
-66.958751 1
< 0.1%
-66.959178 1
< 0.1%
-66.961923 1
< 0.1%
-66.962913 1
< 0.1%
-66.963918 1
< 0.1%
-66.963975 1
< 0.1%

is_fraud
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
1289169 
1
 
7506

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Length

2024-09-15T16:47:04.523488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T16:47:04.622273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1296675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Interactions

2024-09-15T16:46:53.862511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:12.714397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:15.917222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:19.057863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:22.110235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:25.114450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:28.000077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:30.997635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:35.361790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:39.019626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:42.021218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:44.967391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:48.003916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:50.926142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:54.093276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:12.957018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:16.125491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:19.279322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:22.329800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:25.330045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:28.222882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:31.219675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:35.640727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:39.242652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:42.239769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:45.189240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:48.219033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:51.141616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:54.305681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:13.174927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:16.381225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:19.491744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:22.546412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:25.534906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:28.440672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:31.443310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:35.892756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:39.455633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:42.451533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:45.407399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:48.433332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:51.350821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:54.527667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:13.395880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:16.592633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:19.717431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:22.760899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:25.747358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:28.664512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:31.738699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:36.169683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:39.691170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:42.665001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:45.634621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:48.658697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:51.567235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:54.743410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:13.606959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:16.798955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:19.935784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:22.978085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:25.947632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:28.884283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:32.373219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:36.443979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:39.906298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:42.874816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:45.850315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:48.871943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:51.779444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:54.960309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:13.822536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:17.004849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:20.156984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:23.196252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:26.156031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:29.093719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:32.687682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:36.699844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:40.121745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:43.086943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:46.073993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:49.088200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:51.992539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:55.169278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:14.063878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:17.249134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:20.375899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:23.411530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:26.357978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:29.306261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:32.983163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:36.970004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:40.337947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:43.298118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:46.292743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:49.295171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:52.200060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:55.374099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:14.301385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:17.466003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:20.588720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:23.618522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:26.563172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:29.512824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:33.279738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:37.242834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:40.547442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:43.503670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:46.504303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:49.495510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:52.406660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:55.587366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:14.564655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:17.709467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:20.806616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:23.832647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:26.769113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:29.732878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:33.585983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:37.542336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:40.773365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:43.711018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:46.719505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:49.704625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:52.615604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:55.793390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:14.769928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:17.911719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:21.017115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:24.055005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:26.965746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:29.938863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:33.882237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:37.836881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:40.980671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:43.921716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:46.931781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:49.904482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:52.821034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:56.000499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:15.027350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:18.159635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:21.235512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:24.268018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:27.170286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:30.148594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:34.187301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:38.066785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:41.189878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:44.128228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:47.139061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:50.106155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:53.026517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:56.217577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:15.240423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:18.381069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:21.457022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:24.490432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:27.380251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:30.368025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:34.475952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:38.309874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:41.408736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:44.341643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:47.356718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:50.311461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:53.241815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:56.426117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:15.456449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:18.629173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:21.670018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:24.703660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:27.583409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:30.579062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:34.773485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:38.546092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:41.613999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:44.551034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:47.572897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:50.512577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:53.451479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:56.629784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:15.710001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:18.834039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:21.894249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:24.914193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:27.785394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:30.787467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:35.072898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:38.777682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:41.816182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:44.758816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:47.782708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:50.715654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T16:46:53.652279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-15T16:47:04.705256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
amtcategorycc_numcitygenderis_fraudlatlongmerch_latmerch_longmerchantstreettrans_date_trans_timetrans_numunix_timezip
amt1.000-0.295-0.001-0.0020.0000.0000.012-0.0000.0120.000-0.0120.0080.0010.0010.0010.001
category-0.2951.0000.0060.0010.0450.057-0.009-0.000-0.009-0.0000.0320.0020.000-0.0000.0000.002
cc_num-0.0010.0061.0000.0000.0510.006-0.004-0.013-0.004-0.013-0.0010.0040.0020.0010.0020.013
city-0.0020.0010.0001.0000.0570.005-0.042-0.067-0.041-0.067-0.000-0.023-0.0000.001-0.0000.076
gender0.0000.0450.0510.0571.0000.0080.1010.0910.1030.0820.0060.1340.0000.0020.0000.119
is_fraud0.0000.0570.0060.0050.0081.0000.0080.0060.0080.0050.0100.0050.0190.0000.0180.005
lat0.012-0.009-0.004-0.0420.1010.0081.0000.1060.9910.105-0.002-0.0030.001-0.0010.001-0.162
long-0.000-0.000-0.013-0.0670.0910.0060.1061.0000.1060.998-0.0010.074-0.0010.001-0.001-0.959
merch_lat0.012-0.009-0.004-0.0410.1030.0080.9910.1061.0000.104-0.002-0.0050.001-0.0010.001-0.162
merch_long0.000-0.000-0.013-0.0670.0820.0050.1050.9980.1041.000-0.0010.074-0.0010.001-0.001-0.957
merchant-0.0120.032-0.001-0.0000.0060.010-0.002-0.001-0.002-0.0011.0000.001-0.001-0.000-0.0010.001
street0.0080.0020.004-0.0230.1340.005-0.0030.074-0.0050.0740.0011.000-0.001-0.001-0.001-0.052
trans_date_trans_time0.0010.0000.002-0.0000.0000.0190.001-0.0010.001-0.001-0.001-0.0011.000-0.0011.0000.001
trans_num0.001-0.0000.0010.0010.0020.000-0.0010.001-0.0010.001-0.000-0.001-0.0011.000-0.001-0.001
unix_time0.0010.0000.002-0.0000.0000.0180.001-0.0010.001-0.001-0.001-0.0011.000-0.0011.0000.001
zip0.0010.0020.0130.0760.1190.005-0.162-0.959-0.162-0.9570.001-0.0520.001-0.0010.0011.000

Missing values

2024-09-15T16:46:56.784185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-15T16:46:57.630952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trans_date_trans_timecc_nummerchantcategoryamtgenderstreetcityziplatlongtrans_numunix_timemerch_latmerch_longis_fraud
00270318618965209551484.9705685262865436.0788-81.178156438132537601836.011293-82.0483150
116304233373222414107.2304356129916048.8878-118.2105159395132537604449.159047-118.1864620
22388594920576613900220.1116024688325242.1808-112.2620818703132537605143.150704-112.1544810
333534093764340240360245.001930845963246.2306-112.1138544575132537607647.034331-112.5610710
44375534208663984297941.9614182162443338.4207-79.4629831111132537618638.674999-78.6324590
554767265376804500607294.6304712231891740.3750-75.2045124696132537624840.653382-76.1526670
6630074693890476534344.5408823516785137.9931-100.9893667812132537628237.162705-100.1533700
776011360759745864107271.6512242362282438.8432-78.6003552855132537630838.948089-78.5402960
88492271083101120125094.2706854741566540.3359-79.66071277329132537631840.351813-79.9581460
9927208303046816745634198.3902131493704036.5220-87.3490301740132537636137.179198-87.4853810
trans_date_trans_timecc_nummerchantcategoryamtgenderstreetcityziplatlongtrans_numunix_timemerch_latmerch_longis_fraud
12966651274781213193596103206211672.1717376514977545.7549-84.447084021137181652244.938461-83.9962340
12966661274782458765740216534181527457.3006356386095841.0646-87.5917281762137181656240.556811-88.0923390
1296667127478348223677835004582211319.7119603173384428.0758-81.5929268011137181665627.465871-81.5118040
129666812747842131417125845444247100.8507442683907332.1530-90.121768597137181668331.377697-90.5284500
129666912747854400011257587661852598937.3804835846885941.4972-98.7858782641137181669641.728638-99.0396600
1296670127478630263540414123499015.5611543308473537.7175-112.4777344658137181672836.841266-111.6907650
1296671127478760111492064569972151.7018568132179039.2667-77.5101199896137181673938.906881-78.2465280
1296672127478835148659308946955991105.9311583468832532.9396-105.8189366013137181675233.619513-105.1305290
129667312747892720012583106919509174.9014334715775643.3526-102.54111086299137181681642.788940-103.2411600
12966741274790429290257105697320737014.3011277825987145.8433-113.8748726622137181681746.565983-114.1861100